Over the last few months, I have been noticing a pattern in many organisations experimenting with AI. A recent experience brought this out clearly and reminded me why we still need basic engineering discipline even in this new world.

A client reached out to the team I was consulting for a proposal to redevelop a Python application and deploy it on Microsoft Azure. One of their business leaders had created the first version using an AI tool. Since it ran in the browser, the team assumed it was ready for production. When we looked at the files, all we received was a saved webpage (HTML) and part of a Python script. The backend logic, code, DB schema, JavaScript and other supporting pieces were missing, so we had no clue what the prototype was meant to do.

There is nothing wrong in using AI tools like Gemini, Claude or ChatGPT to sketch ideas or validate a workflow. They are wonderful for quick iteration and early exploration. The problem starts when something generated in an AI chat window gets treated as a complete software product.

To build anything that will run securely and reliably inside an enterprise, we still need clarity on the basics. The objective and the business case. The inputs and outputs. The user roles and the workflow. A simple specification. Some understanding of data flow, security and integration needs. Access to the original AI chat sessions also helps. These are not paperwork. They form the bridge between what was intended and what needs to be built.

AI can speed up development, but it does not remove the need for engineering rigour. Systems still need to be stable, scalable and secure.

As more leaders experiment with AI, keeping this distinction in mind will save time and frustration for everyone. Prototypes are a good beginning, not the final product.